Learning Outcomes

The Customer-Led Network Revolution (CLNR) was one of the most significant UK smart grid demonstration projects ever undertaken. CLNR sought to establish the optimum mix of technical, social and commercial interventions needed to support the UK's transtition to a low carbon economy; this knowledge was gained through pursuing five Learning Outcomes and drawing together the findings.

Learning Outcome 1

What are customer’s current, emerging and possible future load and generation characteristics?

The data gathered for LO1 enabled us to update the industry’s current understanding of electricity consumption and generation profiles across a representative cross-section of customer and demographic groups.

The outputs from LO1 were:

A new set of load profiles to update ACE49 (statistical methods for calculating demand and voltage regulation on LV radial distribution systems, 1981). This will enable distribution network operators to improve planning of the low voltage electricity distribution networks, and so keep the cost of connections and network reinforcement as low as possible;

A new set of generation profiles to update ETR130 (the application guide for assessing the capacity of networks containing Distributed Generation, 2006). This will enable distribution network operators to better recognise the contribution that generation makes to the system security of the electricity distribution network, and so keep general network reinforcement costs as low as possible;

A greater understanding of how future economic, social and technological trends are likely to affect the patterns of the various components of load and generation. This will enable distribution network operators to forecast more accurately where it will (and will not) need to reinforce the distribution network

Quantifying the impact on power quality of new disruptive loads such as heat pumps and electric vehicles.

Learning Outcome 2

To what extent are customers flexible in their load and generation, and what is the cost of this flexibility?

LO2 set out to establish the extent to which customers were willing to be flexible in their energy usage and generation, and what the cost of this flexibility might be. To do this, we offered a number of different commercial propositions (including time of use and restricted hours tariffs) to a range of different customer groups including;

Domestic customers

SME customers

Domestic customers with low carbon technologies

Customers with rooftop solar photovoltaic installations, also trialled automatic in-premises balancing. We also worked with a small number of industrial and commercial customers and large distributed generators to test the concepts of responsive load and responsive generation on a larger scale.

The outputs from LO2 were:

An understanding of the degree to which customers accept propositions for flexibility, from time of use and restricted hours tariffs to direct control, and;

An understanding the degree to which customers who have accepted a proposition for flexibility will then respond.

This research allowed us to compare the cost and effectiveness of non-network solutions (i.e. demand response) with network technology solutions, to provide increased electricity distribution network capacity.

Learning Outcome 3

To what extent is the network flexible and what is the cost of this flexibility?

Traditionally, distribution network operators (DNOs) have met new demands placed on the powergrid by reinforcing the network. In this part of the project we exploried smarter alternatives which could allow conventional reinforcement and its costs to be avoided or deferred.

This involved trialling the integration of primary and secondary voltage control, real-time thermal rating and electrical energy storage. These commercially available technologies had previously been deployed individually at higher voltages; the CLNR project delivered important new industry learning by deploying these technologies in combination, in conjunction with customer response, and at lower voltage levels.

So as not to place customers at increased risk, all trials took place on Northern Powergrid networks which were fundamentally sound. For the purposes of the trials we simulated network constraints by calibrating the network controls to artificially tight bands. The intent was to have no observable impact on customers.

The majority of the trials took place on two, specially selected, primary test networks within the Northern Powergrid distribution area. The first, at Denwick in Northumberland, is a 20kV network serving a sparse rural area with a load curve dominated by storage heating and a consumption peak after midnight. The second, at Rise Carr in Teesside, is a 6kV dense urban network with a classic mixed load curve and an early evening peak

Learning Outcome 4

What is the optimum solution to resolve network constraints driven by the transition to a low carbon economy?

This phase of the project focused on expanding and refining the knowledge and outputs from Learning Outcomes 1, 2 and 3. We carried out detailed analysis of the trial results and (other research which became available during the course of the project) to identify the optimum solution for resolving network constraints and we assessed the interaction between customer flexibility and network flexibility to better understand the role which each plays in the overall smart grid solution.

This detailed analysis formed the baseline for our thinking on the role customers, distributors and suppliers will play in the transition to a low carbon economy.

This phase of the project modelled, simulated and emulated technology combinations to gain a more rounded understanding of the issues. Where appropriate the modelling, simulation and emulation work reflected the outcomes of the trials and included combinations not piloted in the field. The modelling, simulation and emulation added value to the practical trials and extended the value of our learning outcomes.

Key learning ouputs

Learning Outcome 5

What are the most effective means to deliver optimal solutions between customer, supplier and distribution network operator?

In LO5, we reviewed the outputs from all the other Learning Outcomes drew our conclusions. We produced the outputs and tools needed to transition CLNR learning into business as usual and identified the optimum solutions for a range of given circumstances, from the non-network and network options available.

We developed a DNO toolbox of practical solutions, guidance and codes of practice that specify what the best solution is likely to be for a given network, with a given set of customers. The key aim of the toolkit is to guide network planners in selecting the optimum non-network, novel network and conventional network solutions. This is necessary not just to allow these new policies to be deployed, but also to facilitate the connection of new low carbon and zero carbon technologies.

We established and published new customer load and generation data which formed the basis for our recommendations to update ACE49 and improve the planning of LV networks, thereby reducing connection and reinforcement costs. We also proposed recommendations for a new set of generation profiles to update ETR130 and ER G59. We provided an understanding and disseminate to other distributors how EAVC, RTTR and EES may be integrated to enable more low-carbon technologies to be accepted onto the network.

We are sharing our understanding of the commercial arrangements of a smart grid and how these arrangements link customers, suppliers and distributors. We have assesed the value of customer flexibility and how this could be shared between participating customers, suppliers, and distributors. We have developed and will share our understanding of the economic structures which would better allow sharing of costs and ensure an appropriate share of network costs and benefits between distributors and customers/suppliers. We have provided a view of the costs associated with these arrangements, and are sharing our views on how commercial models and arrangements might evolve to better facilitate the transition to a low carbon economy.